SOTAVerified

Reinforcement Learning (RL)

Reinforcement Learning (RL) involves training an agent to take actions in an environment to maximize a cumulative reward signal. The agent interacts with the environment and learns by receiving feedback in the form of rewards or punishments for its actions. The goal of reinforcement learning is to find the optimal policy or decision-making strategy that maximizes the long-term reward.

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 10426–10450 of 15113 papers

TitleStatusHype
Runtime Adaptation in Wireless Sensor Nodes Using Structured Learning—0
Variable Gain Gradient Descent-based Reinforcement Learning for Robust Optimal Tracking Control of Uncertain Nonlinear System with Input-Constraints—0
Designing high-fidelity multi-qubit gates for semiconductor quantum dots through deep reinforcement learning—0
Efficient Model-Based Reinforcement Learning through Optimistic Policy Search and PlanningCode1
Analytic Manifold Learning: Unifying and Evaluating Representations for Continuous ControlCode1
MetaCURE: Meta Reinforcement Learning with Empowerment-Driven ExplorationCode1
An online evolving framework for advancing reinforcement-learning based automated vehicle control—0
Pipeline PSRO: A Scalable Approach for Finding Approximate Nash Equilibria in Large GamesCode1
Tackling Morpion Solitaire with AlphaZero-likeRanked Reward Reinforcement Learning—0
Optimistic Distributionally Robust Policy OptimizationCode0
Reinforcement Learning with Supervision from Noisy Demonstrations—0
Non-local Policy Optimization via Diversity-regularized Collaborative Exploration—0
Benchmarking Multi-Agent Deep Reinforcement Learning Algorithms in Cooperative TasksCode1
Adversarial Attacks and Detection on Reinforcement Learning-Based Interactive Recommender Systems—0
Reinforcement Learning as Iterative and Amortised Inference—0
Hindsight Expectation Maximization for Goal-conditioned Reinforcement Learning—0
Bridging Worlds in Reinforcement Learning with Model-Advantage—0
Exchangeable Models in Meta Reinforcement LearningCode0
Explore then Execute: Adapting without Rewards via Factorized Meta-Reinforcement Learning—0
Generalizing Curricula for Reinforcement Learning—0
Learning Intrinsically Motivated Options to Stimulate Policy Exploration—0
Hierarchical reinforcement learning for efficent exploration and transfer—0
Logical Composition in Lifelong Reinforcement Learning—0
StarCraft II Build Order Optimization using Deep Reinforcement Learning and Monte-Carlo Tree Search—0
Systematic Generalisation through Task Temporal Logic and Deep Reinforcement Learning—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1PPGMean Normalized Performance0.76—Unverified
2PPOMean Normalized Performance0.58—Unverified